We study the evolution of opinions inside a population of interacting large
language models (LLMs). Every LLM needs to decide how much funding to allocate
to an item with three initial possibilities: full, partial, or no funding. We
identify biases that drive the exchange of opinions based on the LLM's tendency
to (i) find consensus with the other LLM's opinion, (ii) display caution when
specifying funding, and (iii) consider ethical concerns in its opinion. We find
these biases are affected by the perceived absence of compelling reasons for
opinion change, the perceived willingness to engage in discussion, and the
distribution of allocation values. Moreover, tensions among biases can lead to
the survival of funding for items with negative connotations. We also find that
the final distribution of full, partial, and no funding opinions is more
diverse when an LLM freely forms its opinion after an interaction than when its
opinion is a multiple-choice selection among the three allocation options. In
the latter case, consensus or polarization is generally attained. When agents
are aware of past opinions, they seek to maintain consistency with them, and
more diverse updating rules emerge. Our study is performed using a Llama 3 LLM.

我们研究互动的大型语言模型（LLMs）群体内舆论的演变。我们发现这些模型的交流受到几个倾向性因素的影响，分别是追求其他模型意见的共识、在资金配置时展现谨慎和考虑伦理问题。我们还发现这些偏见受到意见改变缺乏具有说服力的理由、愿意参与讨论的程度以及资源分配的分布的影响。此外，这些偏见之间的紧张关系可能导致对具有负面含义的项目的资金支持继续存在。通过自由形成意见与从三个资源分配选项中选择意见相比，我们发现在互动后，LLM 的意见分布更加多样化，而在后一种情况下，通常会达成共识或极化。当代理人了解过去的意见时，他们会努力保持一致，并产生更多样的更新规则。我们使用 Llama 3 LLM 进行了研究。